Search Results for author:
Found 6 papers, 4 papers with code
Learning Emergent Gaits with Decentralized Phase Oscillators: on the role of Observations, Rewards, and Feedback
We present a minimal phase oscillator model for learning quadrupedal locomotion.
Quality-Diversity through AI Feedback
In many text-generation problems, users may prefer not only a single response, but a diverse range of high-quality outputs from which to choose.
Quality Diversity through Human Feedback
Meanwhile, Quality Diversity (QD) algorithms excel at identifying diverse and high-quality solutions but often rely on manually crafted diversity metrics.
OMNI: Open-endedness via Models of human Notions of Interestingness
An Achilles Heel of open-endedness research is the inability to quantify (and thus prioritize) tasks that are not just learnable, but also $\textit{interesting}$ (e. g., worthwhile and novel).
Robustness of Utilizing Feedback in Embodied Visual Navigation
This paper presents a framework for training an agent to actively request help in object-goal navigation tasks, with feedback indicating the location of the target object in its field of view.
Good Time to Ask: A Learning Framework for Asking for Help in Embodied Visual Navigation
In reality, it is often more efficient to ask for help than to search the entire space to find an object with an unknown location.
